Gender-Affirming Hormone Treatment and Metabolic Syndrome Among Transgender Veterans

This cohort study investigates associations between gender-affirming hormone treatment and development and progression of metabolic syndrome among transgender veterans compared with cisgender veterans.


Introduction
An estimated 0.4% to 0.6% of adults in the US identify as transgender, meaning their gender identity differs from the sex assigned at birth, and this number is increasing. 1,2Many transgender individuals receive gender-affirming hormone treatment (GAHT) to reduce gender dysphoria and improve quality of life. 3Estrogen and antiandrogen hormone replacement is the main form of GAHT for transfeminine individuals, while testosterone replacement is the primary hormone treatment for transmasculine individuals. 4tabolic syndrome refers to a collection of intertwined factors that are directly associated with increased risk of adverse outcomes, including atherosclerotic cardiovascular disease (ASCVD), insulin resistance, type 2 diabetes (T2D), systolic hypertension, and nonalcoholic fatty liver disease (NAFLD). 5,6Metabolic syndrome is typically diagnosed based on abnormal values relative to appropriate cutoff values for at least 3 of the 5 clinical measures: blood pressure (BP), high-density lipoprotein (HDL) cholesterol, triglycerides, blood glucose, and waist circumference.
The binary approach to metabolic syndrome classification makes it difficult to assess worsening of this condition over time.Gurka et al [7][8][9] developed a novel sex-and race-specific metabolic syndrome risk continuous score, which is superior to a binary indicator for metabolic syndrome prediction.
1][12][13][14][15][16] Conditions that cause females to experience reduced estradiol levels, such as menopause or ovariectomy, or increased testosterone levels, such as polycystic ovary syndrome, may promote metabolic syndrome development. 12,14,17In males, testosterone is converted to dihydrotestosterone by 5α-reductase and dihydrotestosterone is converted to estradiol by aromatase.Males with decreased aromatase levels experience greater risk of abdominal obesity, elevated blood lipid levels, and insulin resistance. 13,18,19These metabolic changes are also observed in males receiving antiandrogen treatment for prostate cancer and in males with Klinefelter syndrome (XXY sex chromosomes). 20,21XX chromosome-associated increased risk of metabolic syndrome is further supported by mouse model studies showing that presence of 2 X chromosomes in male mice promotes increased body fat and elevated plasma cholesterol levels. 22,23ltiple studies have investigated the association of GAHT with the individual components of metabolic syndrome and implicated GAHT in metabolic syndrome, 6,24 but data regarding the longitudinal effects of GAHT on metabolic syndrome development and progression are lacking.The current study investigated the longitudinal association of GAHT with changes in metabolic syndrome z-scores in transfeminine and transmasculine individuals compared with cisgender males and females not receiving exogenous sex hormones.The analyses also explored whether the action of exogenous hormones was associated with chromosomes and/or organizational effects of sex hormones that occurred during development.

Methods
This was a retrospective, longitudinal cohort study of transgender and cisgender veterans from the Veterans Health Administration (VHA).The institutional review board (IRB) of the VA Greater Los Angeles Health Care System reviewed and approved the study and waived informed consent

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Gender-Affirming Hormone Treatment and Metabolic Syndrome Among Transgender Veterans   25 To confirm gender identity status of study participants and obtain the feminizing (estradiol) or masculinizing (testosterone) hormone initiation date, we obtained the IRB's permission to access and review patients' medical records.

Cohort Ascertainment and Data Collection
Demographics and comorbidities were extracted from VHA and Centers for Medicare & Medicaid Services (CMS) databases. 26The race and ethnicity data provided for this cohort were selfreported and were included in the analysis because the z-score equation is race dependent.
Categories were Hispanic, non-Hispanic Black (hereafter, Black), non-Hispanic White (hereafter, White), and other (included Asian, Pacific Islander, unknown, and those who declined to answer).A combination of 2 outpatient or 1 inpatient ICD-9 or ICD-10 diagnostic or Current Procedural Terminology code was used to determine preexisting comorbidity status using the VHA and CMS datasets to improve accuracy. 26,27Laboratory results; prescription medications, including sex hormones; body mass index (BMI; calculated as weight in kilograms divided by height in meters squared); and BP were obtained from the VHA Corporate Data Warehouse; waist circumference data were not available.Using the date of hormonal transition as the index date, we accessed data up to 13 years before and 15 years after hormonal transition.Transgender and cisgender participants were matched for race and ethnicity, birth date, and index visit date using 1:1 matching cisgender individual for each transgender participant.Cisgender matches were selected from a pool of 17 000 veterans who had complete BMI, HDL, systolic BP (SBP), triglyceride, and blood glucose data obtained at a proxy index date (we used an outpatient visit within the same quarter of the transgender match's index date).For a given transgender participant, the corresponding cisgender participant had the same race and ethnicity and was selected as the individual with the closest birth date to that of the transgender participant and with laboratory data obtained closest to the hormone transition in the corresponding transgender participant (ie, index date).All birth dates and index dates matched to within 1 year or better.Cisgender participants were not receiving any external sex hormones.

Metabolic Syndrome z-Score Calculations
The metabolic syndrome z-score was adopted from BMI-based equations by Gurka et al. 9 These equations are sex-and race-dependent (White and Black race).We used sex assigned at birth to calculate the metabolic syndrome z-scores before and after transition.We used the formula for White individuals for any participants in the "other" race and ethnicity category, as there were no corresponding formulas for these categories.Time in years was computed from the index date for each transgender participant and the corresponding date for the matched cisgender individual.Year 0 was defined as the year of hormonal transition, corresponding to the year initiated by the index date.Years (and observations) before the hormone transition thus have negative values, such that year −3 corresponds to 3 years before year 0, the transition year, and year 4 is the fourth year after year 0. The standardized metabolic syndrome scores and their SEMs were compared among the 4 groups (transfeminine, transmasculine, cisgender female, and cisgender male) over time (years) before and after transition.The mean values of metabolic syndrome components before and after the index date were calculated using the same model.

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Gender-Affirming Hormone Treatment and Metabolic Syndrome Among Transgender Veterans

Statistical Analysis
Patient characteristics were reported using means and SDs for continuous variables and frequencies and percentages for categorical variables.The mean profiles were computed using a repeated measure (mixed) analysis of variance model.A mixed model was particularly necessary since no participant had the full surveillance time (13 years before to 15 years after the index date) (eTable in Supplement 1).This model included random participant effects to allow for autocorrelation.When participants had several observations across time, the model could compute a correlation-covariance matrix and use this matrix and the data to reconstruct a maximum likelihood estimate of the true mean profile that was unbiased if the incompleteness was not systematic (ie, not much more likely to be missing before vs after transition).The resulting mean profile was an estimate of what would have been observed if each participant had complete follow-up.These models also allowed for the assessment of correlations among multiple observations across time for the same participants.A check of the residual errors confirmed that the errors followed a normal distribution.Plots of smoothed mean profiles were computed using the locally estimated scatterplot smoothing method.
These linear mixed models allowed for the modeling of heterogeneity in the change in metabolic syndrome z-scores across time.
All analyses were conducted using SAS, version 8.3 (SAS Institute Inc).Two-sided P < .05 was considered significant.

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Gender-Affirming Hormone Treatment and Metabolic Syndrome Among Transgender Veterans Longitudinal Changes in Metabolic Syndrome z-Score The smoothed and unsmoothed longitudinal alterations in metabolic syndrome z-scores as time-and age-adjusted mean scores are presented in Figure 1 and Figure 2. Notably, the transmasculine group began with the lowest metabolic syndrome z-score of all groups, with an increase to the highest score at the final time point.The transfeminine and cisgender male groups, both of which were assigned male at birth, had higher metabolic syndrome z-scores at the initial time point; while the cisgender male z-score increase moderately with time, the transfeminine group did not show substantial changes.The individuals who received GAHT experienced the greatest changes in metabolic syndrome z-score values, with testosterone treatment leading to the greatest increase in z-score (in transmasculine individuals) and estradiol treatment leading to the smallest change in z-score (in transfeminine individuals).In a second model, all the metabolic syndrome components and metabolic syndrome z-scores were adjusted for age to account for a possible decrease in sex hormone levels in the cisgender individuals with aging.The Spearman correlation coefficient of age

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Gender-Affirming Hormone Treatment and Metabolic Syndrome Among Transgender Veterans with metabolic syndrome z-score ranged from 0.095 to 0.195 across the transgender and cisgender groups, which indicated a very low positive correlation.Controlling for the effect of group and surveillance time and the random effect of participant, the coefficient (r) for the correlation of age with the (residual) metabolic syndrome z-score was 0.034.The adjustment for age did not change the overall results (Table 3).

Estimation of Future Risk
The metabolic syndrome z-score can be used to estimate the risk of future CVD and T2D.In cohorts of a study by Gurka et al, 9 each 1-SD increase in metabolic syndrome z-score (BMI-based) was associated with a significant increase in the risk of CVD (hazard ratio [HR], 1.78; 95% CI, 1.67-1.90)and T2D (HR, 3.37; 95% CI, 3.15-3.61).Based on this association and metabolic syndrome z-score values expressed in SDs, the transmasculine group in our study, which had a mean (SEM) difference after vs before transition of 0.78 (0.15; P < .001)(Table 2), would have an HR for CVD of 1.58 and for T2D of 3.67 (95% CIs are not presented because they would not take into account the covariance and therefore might not be representative of the true 95% CIs).For the transfeminine group, the calculated HR for CVD would be 1.10, while that for T2D would be 1.22.For the cisgender female group, the HR for CVD would be 1.25 and the HR for T2D would be 1.59.[36] We also observed that individuals with exogenous testosterone administration (transmasculine) had higher metabolic syndrome z-scores than those with endogenous male hormones (cisgender male).Similarly, individuals with exogenous estradiol administration (transfeminine) had lower metabolic z-scores than those with endogenous female hormones (cisgender female) (Table 2).This raises the possibility that the action of exogenous hormones is influenced by chromosomal sex and/or organizational effects of gonadal hormones that occurred during development. 24We also derived methodologic information that is relevant for studies of GAHT effects, including the importance of validating the classification of gender dysphoria and determining the date of GAHT initiation and the value of harvesting longitudinal data.We determined that medical record review may be a critical step in assessing the association of GAHT with clinical outcomes.In particular, we found that ICD-9 and ICD-10 codes are not reliable as the only identifiers of gender dysphoria and that the date of GAHT initiation is key to assessing clinical outcomes before and after hormonal transition.

Limitations
As with most studies of this type, there are limitations.We were unable to draw causal inferences from this observational study.The limited sample size of the transmasculine and cisgender female groups may have led to an attenuated effect size, and we acknowledge that veterans, especially female veterans, are not representative of the general population due to historical patterns of military recruitment and deployment and sociological factors driving service in the military; in addition, veterans in the VHA differ from those not using the VHA. 43We did not take into account minority stress among transgender veterans, which is associated with health and well-being outcomes. 44Future studies should validate the correlation of the metabolic syndrome z-score with incident ASCVD, NAFLD, and T2D; document metabolic syndrome z-scores in transgender persons using waist circumference to account for lean vs fat mass; and explore variation in metabolic syndrome z-scores based on gender identity rather than sex assigned at birth, as changing sex hormones at some point in adult life is likely to have unique effects on health status.

Conclusions
Our data from this cohort study indicated that in both cisgender and transgender individuals, estradiol was associated with reduced metabolic syndrome risk, whereas testosterone was associated with increased risk.These findings are relevant for the management of metabolic syndrome risk factors in cisgender and transgender individuals.It is also important to note that exogenous sex hormones are not equivalent to endogenous sex hormones, nor is the body in which they operate the same as one never exposed to the opposing sex hormones.

Figure 1 .
Figure 1.Adjusted Metabolic Syndrome z-Scores of Transgender and Cisgender Veterans After vs Before the Index Date With Smoothing 1.5

Figure 2 .
Figure 2. Adjusted Metabolic Syndrome z-Score of Transgender and Cisgender Veterans Before and After the Index Date Without Smoothing 1.2 The source population consisted of patients from US VHA databases with any International

Table 2
presents the mean changes in unadjusted metabolic syndrome components before vs after index dates (13 years prior to the index date and up to 15 years after the index date) and comparisons among groups.Transmasculine participants had the greatest mean (SEM) increase in BMI after transition(2.3[0.6];P < .001),whereas BMI did not change significantly in transfeminine individuals (0.3 [0.2]; P = .13).Transmasculine participants had a significant reduction in HDL levels (mean

Table 3 .
33e-Adjusted Within-Group Changes Before and After GAHT and Between-Group Differences in Within-Group Change in lipid profiles, whereas transfeminine participants experienced favorable changes, and no changes in BP occurred in either group.In 2017, Maraka et al33performed a meta-analysis of 29 eligible studies that included 3231 transfeminine and 1500 transmasculine individuals.These authors reported that GAHT in transmasculine persons was associated with increased serum triglyceride and low-density lipoprotein levels after 24 months of treatment, whereas HDL levels decreased.Smaller studies (<50 persons in each group) also reported that transfeminine individuals experienced more favorable changes in metabolic syndrome components following GAHT than did transmasculine individuals.